AI-BASED SMART STUDENT ERP SYSTEM WITH ACADEMIC RISK PREDICTION AND AUTOMATED PARENT NOTIFICATION

In modern educational institutions, managing student attendance and academic
performance manually is time-consuming, inefficient, and prone to human error. Traditional systems
often lack real-time communication with parents and do not provide predictive insights into student
outcomes. To address these limitations, this project proposes an AI-Based Smart Student ERP System
that automates attendance tracking, performance monitoring, and parent notifications while
integrating predictive analytics for early academic risk detection. The system is developed using
Python and the Django framework, with a structured database to store student information, attendance
records, examination marks, and assignment scores. Teachers can record attendance digitally,
reducing paperwork and administrative workload. When a student is marked absent, the system
automatically sends an SMS or WhatsApp notification to the parent, ensuring immediate
communication and improved accountability. In addition to automation, the system incorporates
Machine Learning techniques to analyze attendance percentages and academic performance data. A
Logistic Regression model is used to predict whether a student is at academic risk. If the model
identifies a high probability of poor performance or potential failure, an automated alert is sent to
parents to enable timely intervention. The system also includes a basic AI chatbot module that allows
parents to access attendance and academic details through automated responses. By combining
automation, predictive analytics, and intelligent communication features, the proposed system
enhances efficiency, strengthens parent-school collaboration, and supports proactive academic
management.